Machine Learning-Accelerated Band-Edge Engineering of Pnictogen Chalcohalide Solid Solutions for Solar Energy Technologies

Summary

arXiv:2608.16611v1 Announce Type: new Abstract: Pnictogen chalcohalide (MChX; M=Bi,Sb; Ch=S,Se; X=I,Br) solid solutions combine earth-abundant constituents, tunable band gaps ($1.2$-$2.1$ eV), and strong optical absorption, making them attractive for solar energy conversion. Yet their vast compositional space has so far prevented a systematic assessment of how band-edge positions vary with stoichiometry and surface termination. Here, we combine first-principles density functional theory with machine learning to predict the valence and conduction band-edge positions of $\mathrm{Bi}_x\mathrm{Sb}_{1-x}\mathrm{S}_y\mathrm{Se}_{1-y}\mathrm{I}_z\mathrm{Br}_{1-z}$ solid solutions across their full compositional range on the two most stable surfaces, (010) and (011).